---
title: SageMaker Tracking
---

>[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service that is used to quickly and easily build, train and deploy machine learning (ML) models.

>[Amazon SageMaker Experiments](https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html) is a capability of `Amazon SageMaker` that lets you organize, track, compare and evaluate ML experiments and model versions.

This notebook shows how LangChain Callback can be used to log and track prompts and other LLM hyperparameters into `SageMaker Experiments`. Here, we use different scenarios to showcase the capability:

* **Scenario 1**: *Single LLM* - A case where a single LLM model is used to generate output based on a given prompt.
* **Scenario 2**: *Sequential Chain* - A case where a sequential chain of two LLM models is used.
* **Scenario 3**: *Agent with Tools (Chain of Thought)* - A case where multiple tools (search and math) are used in addition to an LLM.

In this notebook, we will create a single experiment to log the prompts from each scenario.

## Installation and Setup

```python
%pip install -qU  sagemaker
%pip install -qU  langchain-openai
%pip install -qU  google-search-results
```

First, setup the required API keys

* OpenAI: [platform.openai.com/account/api-keys](https://platform.openai.com/account/api-keys) (For OpenAI LLM model)
* Google SERP API: [serpapi.com/manage-api-key](https://serpapi.com/manage-api-key) (For Google Search Tool)

```python
import os

## Add your API keys below
os.environ["OPENAI_API_KEY"] = "<ADD-KEY-HERE>"
os.environ["SERPAPI_API_KEY"] = "<ADD-KEY-HERE>"
```

```python
from langchain_community.callbacks.sagemaker_callback import SageMakerCallbackHandler
```

```python
from langchain.agents import initialize_agent, load_tools
from langchain.chains import LLMChain, SimpleSequentialChain
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI
from sagemaker.analytics import ExperimentAnalytics
from sagemaker.experiments.run import Run
from sagemaker.session import Session
```

## LLM Prompt Tracking

```python
# LLM Hyperparameters
HPARAMS = {
    "temperature": 0.1,
    "model_name": "gpt-3.5-turbo-instruct",
}

# Bucket used to save prompt logs (Use `None` is used to save the default bucket or otherwise change it)
BUCKET_NAME = None

# Experiment name
EXPERIMENT_NAME = "langchain-sagemaker-tracker"

# Create SageMaker Session with the given bucket
session = Session(default_bucket=BUCKET_NAME)
```

### Scenario 1 - LLM

```python
RUN_NAME = "run-scenario-1"
PROMPT_TEMPLATE = "tell me a joke about {topic}"
INPUT_VARIABLES = {"topic": "fish"}
```

```python
with Run(
    experiment_name=EXPERIMENT_NAME, run_name=RUN_NAME, sagemaker_session=session
) as run:
    # Create SageMaker Callback
    sagemaker_callback = SageMakerCallbackHandler(run)

    # Define LLM model with callback
    llm = OpenAI(callbacks=[sagemaker_callback], **HPARAMS)

    # Create prompt template
    prompt = PromptTemplate.from_template(template=PROMPT_TEMPLATE)

    # Create LLM Chain
    chain = LLMChain(llm=llm, prompt=prompt, callbacks=[sagemaker_callback])

    # Run chain
    chain.run(**INPUT_VARIABLES)

    # Reset the callback
    sagemaker_callback.flush_tracker()
```

### Scenario 2 - Sequential Chain

```python
RUN_NAME = "run-scenario-2"

PROMPT_TEMPLATE_1 = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.
Title: {title}
Playwright: This is a synopsis for the above play:"""
PROMPT_TEMPLATE_2 = """You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.
Play Synopsis: {synopsis}
Review from a New York Times play critic of the above play:"""

INPUT_VARIABLES = {
    "input": "documentary about good video games that push the boundary of game design"
}
```

```python
with Run(
    experiment_name=EXPERIMENT_NAME, run_name=RUN_NAME, sagemaker_session=session
) as run:
    # Create SageMaker Callback
    sagemaker_callback = SageMakerCallbackHandler(run)

    # Create prompt templates for the chain
    prompt_template1 = PromptTemplate.from_template(template=PROMPT_TEMPLATE_1)
    prompt_template2 = PromptTemplate.from_template(template=PROMPT_TEMPLATE_2)

    # Define LLM model with callback
    llm = OpenAI(callbacks=[sagemaker_callback], **HPARAMS)

    # Create chain1
    chain1 = LLMChain(llm=llm, prompt=prompt_template1, callbacks=[sagemaker_callback])

    # Create chain2
    chain2 = LLMChain(llm=llm, prompt=prompt_template2, callbacks=[sagemaker_callback])

    # Create Sequential chain
    overall_chain = SimpleSequentialChain(
        chains=[chain1, chain2], callbacks=[sagemaker_callback]
    )

    # Run overall sequential chain
    overall_chain.run(**INPUT_VARIABLES)

    # Reset the callback
    sagemaker_callback.flush_tracker()
```

### Scenario 3 - Agent with Tools

```python
RUN_NAME = "run-scenario-3"
PROMPT_TEMPLATE = "Who is the oldest person alive? And what is their current age raised to the power of 1.51?"
```

```python
with Run(
    experiment_name=EXPERIMENT_NAME, run_name=RUN_NAME, sagemaker_session=session
) as run:
    # Create SageMaker Callback
    sagemaker_callback = SageMakerCallbackHandler(run)

    # Define LLM model with callback
    llm = OpenAI(callbacks=[sagemaker_callback], **HPARAMS)

    # Define tools
    tools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=[sagemaker_callback])

    # Initialize agent with all the tools
    agent = initialize_agent(
        tools, llm, agent="zero-shot-react-description", callbacks=[sagemaker_callback]
    )

    # Run agent
    agent.run(input=PROMPT_TEMPLATE)

    # Reset the callback
    sagemaker_callback.flush_tracker()
```

## Load Log Data

Once the prompts are logged, we can easily load and convert them to Pandas DataFrame as follows.

```python
# Load
logs = ExperimentAnalytics(experiment_name=EXPERIMENT_NAME)

# Convert as pandas dataframe
df = logs.dataframe(force_refresh=True)

print(df.shape)
df.head()
```

As can be seen above, there are three runs (rows) in the experiment corresponding to each scenario. Each run logs the prompts and related LLM settings/hyperparameters as json and are saved in s3 bucket. Feel free to load and explore the log data from each json path.

```python

```
